Mercurial > octave
view scripts/linear-algebra/condest.m @ 28152:4609d001daee stable
condest.m: Fix estimate when matrix is not symmetric (bug #57968).
* condest.m (solve_sparse, solve_not_sparse): In switch statement, swap the
calculation method between "notransp" and "transp" cases. Add regression
BIST tests.
author | Rik <rik@octave.org> |
---|---|
date | Wed, 11 Mar 2020 13:11:38 -0700 |
parents | bd51beb6205e |
children | 687d452070c9 7096f672f611 |
line wrap: on
line source
######################################################################## ## ## Copyright (C) 2007-2020 The Octave Project Developers ## ## See the file COPYRIGHT.md in the top-level directory of this ## distribution or <https://octave.org/copyright/>. ## ## This file is part of Octave. ## ## Octave is free software: you can redistribute it and/or modify it ## under the terms of the GNU General Public License as published by ## the Free Software Foundation, either version 3 of the License, or ## (at your option) any later version. ## ## Octave is distributed in the hope that it will be useful, but ## WITHOUT ANY WARRANTY; without even the implied warranty of ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the ## GNU General Public License for more details. ## ## You should have received a copy of the GNU General Public License ## along with Octave; see the file COPYING. If not, see ## <https://www.gnu.org/licenses/>. ## ######################################################################## ## -*- texinfo -*- ## @deftypefn {} {@var{cest} =} condest (@var{A}) ## @deftypefnx {} {@var{cest} =} condest (@var{A}, @var{t}) ## @deftypefnx {} {@var{cest} =} condest (@var{A}, @var{solvefun}, @var{t}, @var{p1}, @var{p2}, @dots{}) ## @deftypefnx {} {@var{cest} =} condest (@var{Afcn}, @var{solvefun}, @var{t}, @var{p1}, @var{p2}, @dots{}) ## @deftypefnx {} {[@var{cest}, @var{v}] =} condest (@dots{}) ## ## Estimate the 1-norm condition number of a square matrix @var{A} using ## @var{t} test vectors and a randomized 1-norm estimator. ## ## The optional input @var{t} specifies the number of test vectors (default 5). ## ## If the matrix is not explicit, e.g., when estimating the condition number of ## @var{A} given an LU@tie{}factorization, @code{condest} uses the following ## functions: ## ## @itemize @minus ## @item @var{Afcn} which must return ## ## @itemize @bullet ## @item ## the dimension @var{n} of @var{a}, if @var{flag} is @qcode{"dim"} ## ## @item ## true if @var{a} is a real operator, if @var{flag} is @qcode{"real"} ## ## @item ## the result @code{@var{a} * @var{x}}, if @var{flag} is "notransp" ## ## @item ## the result @code{@var{a}' * @var{x}}, if @var{flag} is "transp" ## @end itemize ## ## @item @var{solvefun} which must return ## ## @itemize @bullet ## @item ## the dimension @var{n} of @var{a}, if @var{flag} is @qcode{"dim"} ## ## @item ## true if @var{a} is a real operator, if @var{flag} is @qcode{"real"} ## ## @item ## the result @code{@var{a} \ @var{x}}, if @var{flag} is "notransp" ## ## @item ## the result @code{@var{a}' \ @var{x}}, if @var{flag} is "transp" ## @end itemize ## @end itemize ## ## The parameters @var{p1}, @var{p2}, @dots{} are arguments of ## @code{@var{Afcn} (@var{flag}, @var{x}, @var{p1}, @var{p2}, @dots{})} ## and @code{@var{solvefcn} (@var{flag}, @var{x}, @var{p1}, @var{p2}, ## @dots{})}. ## ## The principal output is the 1-norm condition number estimate @var{cest}. ## ## The optional second output is an approximate null vector when @var{cest} is ## large; it satisfies the equation ## @code{norm (A*v, 1) == norm (A, 1) * norm (@var{v}, 1) / @var{est}}. ## ## Algorithm Note: @code{condest} uses a randomized algorithm to approximate ## the 1-norms. Therefore, if consistent results are required, the ## @qcode{"state"} of the random generator should be fixed before invoking ## @code{condest}. ## ## References: ## ## @itemize ## @item ## @nospell{N.J. Higham and F. Tisseur}, @cite{A Block Algorithm ## for Matrix 1-Norm Estimation, with an Application to 1-Norm ## Pseudospectra}. SIMAX vol 21, no 4, pp 1185--1201. ## @url{https://dx.doi.org/10.1137/S0895479899356080} ## ## @item ## @nospell{N.J. Higham and F. Tisseur}, @cite{A Block Algorithm ## for Matrix 1-Norm Estimation, with an Application to 1-Norm ## Pseudospectra}. @url{https://citeseer.ist.psu.edu/223007.html} ## @end itemize ## ## @seealso{cond, norm, normest1, normest} ## @end deftypefn ## Code originally licensed under: ## ## Copyright (c) 2007, Regents of the University of California ## All rights reserved. ## ## Redistribution and use in source and binary forms, with or without ## modification, are permitted provided that the following conditions ## are met: ## ## * Redistributions of source code must retain the above copyright ## notice, this list of conditions and the following disclaimer. ## ## * Redistributions in binary form must reproduce the above ## copyright notice, this list of conditions and the following ## disclaimer in the documentation and/or other materials provided ## with the distribution. ## ## * Neither the name of the University of California, Berkeley nor ## the names of its contributors may be used to endorse or promote ## products derived from this software without specific prior ## written permission. ## ## THIS SOFTWARE IS PROVIDED BY THE REGENTS AND CONTRIBUTORS ``AS IS'' ## AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED ## TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A ## PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS AND ## CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, ## SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT ## LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF ## USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ## ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, ## OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT ## OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF ## SUCH DAMAGE. ## Author: Jason Riedy <ejr@cs.berkeley.edu> ## Keywords: linear-algebra norm estimation ## Version: 0.2 function [cest, v] = condest (varargin) if (nargin < 1 || nargin > 6) print_usage (); endif have_A = false; have_t = false; have_apply_normest1 = false; have_solve_normest1 = false; if (isnumeric (varargin{1})) A = varargin{1}; if (! issquare (A)) error ("condest: A must be square"); endif have_A = true; n = rows (A); if (nargin > 1) if (is_function_handle (varargin{2})) solve = varargin{2}; have_solve_normest1 = true; if (nargin > 2) t = varargin{3}; have_t = true; endif else t = varargin{2}; have_t = true; real_op = isreal (A); endif else real_op = isreal (A); endif elseif (is_function_handle (varargin{1})) if (nargin == 1) error("condest: must provide SOLVEFCN when using AFCN"); endif apply = varargin{1}; have_apply_normest1 = true; if (! is_function_handle (varargin{2})) error("condest: SOLVEFCN must be a function handle"); endif solve = varargin{2}; have_solve_normest1 = true; n = apply ("dim", [], varargin{4:end}); if (nargin > 2) t = varargin{3}; have_t = true; endif else error ("condest: first argument must be a square matrix or function handle"); endif if (! have_t) t = min (n, 5); endif ## Disable warnings which may be emitted during calculation process. warning ("off", "Octave:nearly-singular-matrix", "local"); if (! have_solve_normest1) ## prepare solve in normest1 form if (issparse (A)) [L, U, P, Pc] = lu (A); solve = @(flag, x) solve_sparse (flag, x, n, real_op, L, U, P, Pc); else [L, U, P] = lu (A); solve = @(flag, x) solve_not_sparse (flag, x, n, real_op, L, U, P); endif ## Check for singular matrices before continuing if (any (diag (U) == 0)) cest = Inf; v = []; return; endif endif if (have_A) Anorm = norm (A, 1); else Anorm = normest1 (apply, t, [], varargin{4:end}); endif [Ainv_norm, v, w] = normest1 (solve, t, [], varargin{4:end}); cest = Anorm * Ainv_norm; if (isargout (2)) v = w / norm (w, 1); endif endfunction function value = solve_sparse (flag, x, n, real_op, L , U , P , Pc) ## FIXME: Sparse algorithm is less accurate than full matrix version ## See BIST test for non-orthogonal matrix where relative tolerance ## of 1e-12 is used for sparse, but 4e-16 for full matrix. switch (flag) case "dim" value = n; case "real" value = real_op; case "notransp" value = Pc' * (U \ (L \ (P * x))); case "transp" value = P' * (L' \ (U' \ (Pc * x))); endswitch endfunction function value = solve_not_sparse (flag, x, n, real_op, L, U, P) switch (flag) case "dim" value = n; case "real" value = real_op; case "notransp" value = U \ (L \ (P * x)); case "transp" value = P' * (L' \ (U' \ x)); endswitch endfunction ## Note: These test bounds are very loose. There is enough randomization to ## trigger odd cases with hilb(). %!function value = apply_fun (flag, x, A, m) %! if (nargin == 3) %! m = 1; %! endif %! switch (flag) %! case "dim" %! value = length (A); %! case "real" %! value = isreal (A); %! case "notransp" %! value = x; for i = 1:m, value = A * value;, endfor %! case "transp" %! value = x; for i = 1:m, value = A' * value;, endfor %! endswitch %!endfunction %!function value = solve_fun (flag, x, A, m) %! if (nargin == 3) %! m = 1; %! endif %! switch (flag) %! case "dim" %! value = length (A); %! case "real" %! value = isreal (A); %! case "notransp" %! value = x; for i = 1:m, value = A \ value;, endfor %! case "transp" %! value = x; for i = 1:m, value = A' \ value;, endfor %! endswitch %!endfunction %!test %! N = 6; %! A = hilb (N); %! cA = condest (A); %! cA_test = norm (inv (A), 1) * norm (A, 1); %! assert (cA, cA_test, -2^-8); %!test %! N = 12; %! A = hilb (N); %! [~, v] = condest (A); %! x = A*v; %! assert (norm (x, inf), 0, eps); %!test %! N = 6; %! A = hilb (N); %! solve = @(flag, x) solve_fun (flag, x, A); %! cA = condest (A, solve); %! cA_test = norm (inv (A), 1) * norm (A, 1); %! assert (cA, cA_test, -2^-6); %!test %! N = 6; %! A = hilb (N); %! apply = @(flag, x) apply_fun (flag, x, A); %! solve = @(flag, x) solve_fun (flag, x, A); %! cA = condest (apply, solve); %! cA_test = norm (inv (A), 1) * norm (A, 1); %! assert (cA, cA_test, -2^-6); %!test # parameters for apply and solve functions %! N = 6; %! A = hilb (N); %! m = 2; %! cA = condest (@apply_fun, @solve_fun, [], A, m); %! cA_test = norm (inv (A^2), 1) * norm (A^2, 1); %! assert (cA, cA_test, -2^-6); ## Test singular matrices %!test <*46737> %! A = [ 0 0 0 %! 0 3.33333 0.0833333 %! 0 0.0833333 1.66667]; %! [cest, v] = condest (A); %! assert (cest, Inf); %! assert (v, []); ## Test non-orthogonal matrices %!test <*57968> %! A = reshape (sqrt (0:15), 4, 4); %! cexp = norm (A, 1) * norm (inv (A), 1); %! cest = condest (A); %! assert (cest, cexp, -2*eps); %!test <*57968> %! As = sparse (reshape (sqrt (0:15), 4, 4)); %! cexp = norm (As, 1) * norm (inv (As), 1); %! cest = condest (As); %! assert (cest, cexp, -1e-12); ## Test input validation %!error condest () %!error condest (1,2,3,4,5,6,7) %!error <A must be square> condest ([1 2]) %!error <must provide SOLVEFCN when using AFCN> condest (@sin) %!error <SOLVEFCN must be a function handle> condest (@sin, 1) %!error <argument must be a square matrix or function handle> condest ({1})